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检索条件"任意字段=Conference on Radar Sensor Technology and Data Visualization"

共 4465 条记录，以下是91-100 订阅

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Determining Misalignment State of Automotive radar sensor Using DNN

Determining Misalignment State of Automotive Radar Sensor Us...

引用

IEEE sensors conference

作者： Kim, Junho Park, Chanul Lee, Seongwook Jeong, Taewon Chung Ang Univ Coll ICT Engn Sch Elect & Elect Engn Seoul South Korea Korea Aerosp Univ Coll Engn Sch Elect & Informat Engn Goyang Si Gyeonggi Do South Korea

ISBN: (纸本)9798350303872

An automotive radar sensor can be misaligned compared to the initial installation state due to various external shocks while driving, and it can cause deterioration of radar detection performance. To guarantee the stable detection performance of the radar, a method of estimating the deviation angle compared to the initial state is required. To directly check the radar misalignment, an inefficient bumper removal process is required, so a method of indirectly determining the mounting state of the radar is required. Therefore, in this paper, we propose a method for estimating the tilt angle of the radar sensor using deep neural networks (DNNs). First, radar sensor data are obtained at various radar tilt angles and measurement distances to identify the characteristics of received signals. Then, we extract range profiles from the received signals and design a DNN-based estimator using the profiles as input vectors. The proposed angle estimator consists of several DNNs in parallel, and the input vector passes through one of them according to the distance estimated from the range profile. Finally, the DNN determines the tilt angle for the input vector. In our datasets, the average classification accuracy of the proposed DNN-based classifier is over 98%.

关键词： automotive radar sensor deep neural network tilt angle

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Improving Air-writing Accuracy through data Regression and Interpolation in a Single radar System 18

Improving Air-writing Accuracy through Data Regression and I...

引用

18th European conference on Antennas and Propagation (EuCAP)

作者： Kwak, Seungheon Park, Chanul Lee, Seongwook Chung Ang Univ Coll ICT Engn Sch Elect & Elect Engn Seoul South Korea

ISBN: (纸本)9788831299091;9798350394436

In the radar-based air-writing, the hand movement may not be completely detected depending on the transmission cycle of the radar waveform, which potentially reduces the legibility of the air-writing results. Therefore, in this paper, we propose a method of interpolating the unmeasured portions of the air-writing using polynomial regression. First, using a radar sensor, we acquire range and angle detection results for hand motion over time. Then, the trajectory of the hand motion is expressed in two-dimensional distance coordinates using the range and angle information. Subsequently, the polynomial regression is used to derive the relationship between the observation time and the change of distance coordinates in each dimension. Next, we interpolate the unmeasured portions using the derived regression model and generate the interpolated air-writing results. Finally, to verify the performance of the proposed method, the recognition accuracy of non-interpolated and interpolated air-writing results is evaluated. In other words, we evaluate the recognition accuracy when the two results are input to a convolutional neural network trained with the modified national institute of standards and technology database. The average recognition accuracy of interpolated air-writing results was 65.4%p higher than that of non-interpolated air-writing results.

关键词： air-writing data interpolation polynomial regression single radar sensor

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Deep Learning-based Anomaly Detection in radar data with radar-Camera Fusion 28

Deep Learning-based Anomaly Detection in Radar Data with Rad...

引用

28th Asia-Pacific conference on Communications (APCC)

作者： Ning, Dian Han, Dong Seog Kyungpook Natl Univ Sch Elect & Elect Engn Daegu South Korea

ISBN: (纸本)9798350382617

sensors such as cameras, lidars, and radars are crucial to understanding driving situations in autonomous vehicles. These sensors are susceptible to external and internal abnormalities, potentially leading to severe traffic accidents. A radar sensor is inevitably affected by the obstruction caused by small objects, which can cause the system to malfunction. This paper presents a deep learning approach for detecting anomalies in radar data. The accuracy of anomaly detection is improved by using radar-camera fusion. Our proposed model detects the data anomaly by calculating the deviation from the standard radar cross section (RCS) range. The result demonstrates that the model is capable of identifying the normal range of radar signal and anomaly signal under several different obtained features situations. It enables the detection of potential hazards and warns of dangers to drivers and higher-level control systems, creating a more resilient environment for ensuring autonomous driving safety.

关键词： Anomaly Detection radar Cross Section (RCS) sensor Fusion

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Research on Automotive Safety Based on Multi-sensor Information Fusion technology

Research on Automotive Safety Based on Multi-Sensor Informat...

引用

2024 Asia-Pacific conference on Software Engineering, Social Network Analysis and Intelligent Computing, SSAIC 2024

作者： Can, Cui Peng, Weiqiang Bi, Tenfei Zhu, Haitao China National Accreditation Service for Conformity Assessment Beijing100062 China China Automotive Technology and Research Center Co. Ltd Tianjin300300 China

ISBN: (纸本)9798350393682

This paper aims to explore the automotive safety research based on multi-sensor information fusion technology, which can integrate the information of multiple sensors to improve the accuracy and reliability to better support the safety control of the vehicle. This paper first introduces the application of sensor technology in the field of automotive safety, including radar, camera, lidar, etc. Then, the principle and implementation of multi-sensor information fusion technology are elaborated in detail, including data fusion, feature fusion and decision fusion. Finally, this paper discusses the application of multi-sensor information fusion technology in the field of automotive safety, such as vehicle status, driver behavior, traffic conditions and so on. In conclusion, this paper presents an automotive safety research study based on multi-sensor information fusion technology, aiming to improve vehicle safety performance and contribute to the development of the automotive industry. © 2024 IEEE.

关键词： Vehicle safety

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data-driven Soil Moisture Sensing with mmWave radar 24

Data-driven Soil Moisture Sensing with mmWave Radar

引用

11th ACM International conference on Systems for Energy-Efficient Buildings, Cities, and Transportation, BuildSys 2024

作者： Zhuang, Yujie Zhao, Yang Liu, Jie Harbin Institute of Technology Guangdong Shenzhen China

ISBN: (纸本)9798400707063

Existing soil moisture sensing methods require either plugging probes into soil or burying sensor nodes or aluminum plate in soil. These methods either have mobility and maintenance limitations or have risks of battery leakage. To address these issues, we propose to build a soil moisture sensing dataset using millimeter-wave radar for non-invasive and non-contact moisture sensing of shallow soil. Our millimeter-wave radar soil moisture dataset includes 20 different moisture levels, filling the gap in mmWave radar-based soil moisture sensing datasets. Furthermore, we propose a mm-SoilNet model, which uses the data-driven approach to extract soil moisture features from radar data to estimate the volumetric water content of soil. Our initial experimental results show that the system achieves a mean absolute error of 5.506%. © 2024 Copyright held by the owner/author(s).

关键词： Soil moisture

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Automotive radar data Communication and Simulation Using ROS 8th

Automotive Radar Data Communication and Simulation Using RO...

引用

8th International conference on Information and Communication technology for Intelligent Systems, ICTIS 2024

作者： Sanjaya, D.S. Hullikuppi, Prashanth Joshi, Abhay Kulkarni, Nitish D. Basawaraj School of Electronics and communication Engineering KLE Technological University Karnataka Hubballi India

ISBN: (纸本)9789819766833

In the rapidly advancing field of autonomous driving technologies, the demand for more reliable and robust systems for sensing different parameters has witnessed a significant upsurge. Particularly, the reliability and precision for sensing the objects and their parameters like relative position and distance in different weather conditions increase the requirement of such systems. In response, a comprehensive framework that not only ensures reliability and precision but also provides visual insights into the monitored area with scale-able features has become necessary to build, prototype, and develop prominent perception-based autonomous applications. Thus, our proposed solution provides an efficient way to simulate the communication of an edge radar sensor with the Electronic Control Unit (ECU), which is further responsible for further actions based on the perceived data, using the Robot Operating System (ROS). Our solution also includes a framework that deals with the temperature management of the radar sensor to keep the radar in optimum condition when the radar temperature goes out of the operating temperature range. Integration of ROS with radar data serves as an economic, industry standard, and a better-performing solution. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Automotive radar

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A method for matching low-resolution radar point cloud with optical image information

A method for matching low-resolution radar point cloud with ...

引用

2024 International conference on Image, Signal Processing, and Pattern Recognition, ISPP 2024

作者： Wang, Yangyang Yang, Peiqi Ma, Erke Zhang, Wengang School of Information Science and Technology University of Science and Technology of China Hefei230037 China National University of Defense Technology Hefei230037 China

ISBN: (纸本)9781510680425

The fusion of millimeter-wave radar and optical camera is a challenge in the multi-sensor data fusion *** this paper a new space calibration algorithm is proposed to match the low-resolution radar point cloud information and the optical image *** paper proposes that fusing the wheeled odometer and radar to increase the density of point ***, the real 3D model information of marker and radar point cloud are fitted to extract the 3D feature point of marker, and the 2D pixel feature point of the marker is extracted by Instance Segmentation ***, the marker's 3D-2D matching points are used to solve the external parameters between the radar and *** on Turtlebot2 hardware platform, the experimental results show that the proposed method can calibrate the radar and camera, and match the radar point cloud and optical image information under the condition of large resolution *** proposed algorithm has practical application value in autonomous driving, such as target detection and tracking, scene 3D reconstruction, path planning and navigation. © 2024 SPIE.

关键词： radar imaging

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RF Sensing of Personalized Mobility: Accounting for Temporal Variability in Ethogram-Based Classification 28

RF Sensing of Personalized Mobility: Accounting for Temporal...

引用

conference on radar sensor technology XXVIII

作者： Kurtoglu, Emre Salehin, Sultanus Amin, Moeness G. Gurbuz, Sevgi Z. Univ Alabama Dept Elect & Comp Engn Tuscaloosa AL 35487 USA Villanova Univ Dept Elect & Comp Engn Villanova PA USA

ISBN: (纸本)9781510674158;9781510674141

Human activity recognition (HAR) with radar-based technologies has become a popular research area in the past decade. However, the objective of these studies are often to classify human activity for anyone;thus, models are trained using data spanning as broad a swath of people and mobility profiles as possible. In contrast, applications of HAR and gait analysis to remote health monitoring require characterization of the person-specific qualities of a person's activities and gait, which greatly depends on age, health and agility. In fact, the speed or agility with which a person moves can be an important health indicator. In this study, we propose a multi-input multi-task deep learning framework to simultaneously learn a person's activity and agility. In this initial study, we consider three different agility states: slow, nominal, and fast. It is shown that joint learning of agility and activity improves the classification accuracy for both activity and agility recognition tasks. To the best of our knowledge, this study is the first work considering both agility characterization and personalized activity recognition using RF sensing.

关键词： deep learning radar human activity recognition micro-doppler spectrograms

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Intelligent Excavation Monitoring System Based on Multi-sensor data Fusion 4

Intelligent Excavation Monitoring System Based on Multi-Sens...

引用

4th IEEE International conference on Electronic technology, Communication and Information, ICETCI 2024

作者： Wang, Xiaorui Zhang, Meng Li, Zhuo Li, Gang Tao, Yi Shanghai Tongji Project Management Consulting Co. Ltd. Shanghai China Tongji University College of Electronic and Information Engineering Shanghai China

ISBN: (纸本)9798350361643

A smart excavation monitoring system based on multi-sensor data fusion is introduced in this work. This system includes an intelligent monitoring platform, safety factor evaluation algorithm, various types of excavation sensors, and cameras. The system is used to collect structural data of construction excavations and upload it to a data storage management server. By analyzing and fusing heterogeneous data from multiple sources, the system provides personnel safety monitoring and structural safety monitoring of the excavations. The intelligent detection platform presents the results of data collection and analysis, offering features such as historical data retrieval, real-time data viewing, data visualization through charts, and sensor parameter configuration. The use of multi-sensor data fusion technology avoids the loss of correlation information between data caused by independent analysis, addressing the issue of insufficiently intuitive presentation of excavation conditions. © 2024 IEEE.

关键词： Excavation

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PEOPLE COUNTING SYSTEM USING MMWAVE MIMO radar WITH 3D CONVOLUTIONAL NEURAL NETWORK 97

PEOPLE COUNTING SYSTEM USING MMWAVE MIMO RADAR WITH 3D CONVO...

引用

97th IEEE Vehicular technology conference (VTC-Spring)

作者： Shih, Cheng-Che Zhou, Xinrui Nguyen, Thinh Pham, Khanh Oregon State Univ Sch Elect Engn & Comp Sci Corvallis OR 97331 USA

ISBN: (纸本)9798350311143

In recent years, the number of people counting systems in deployment has been increasing significantly. People counting systems can be used to automate the data collection for advertisement, and revenue projections as well as reduce energy costs using adaptive HVAC operations. However, a naive implementation of people counting systems may result in revealing some unintended information about the users/customers and higher power consumption from operating the systems continuously. In this paper, we study a mmWave Multiple Input Multiple Output (MIMO) radar sensor system for detecting the number of people in a confined space with the aims of low power consumption and minimal leakage of user information. In particular, we showed that a 3D convolutional neural network can accurately determine up to 4 people in a typical size room using a surprisingly minimal number of mmWave signatures ( less than 10) as its inputs.

关键词： People Counting CNN mmWave radar sensor

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